1. Introduction to COVID-19 World Vaccine Adverse Reactions Dataset

2. Project work flow

3. Project methods

   3.1 Overview of important packages and verbs used

   3.2 Challenges and solutions - Load, Clean and Augment

4. Visualizations

5. Modeling

6. Conclusion and discussion

• Found on Kaggle (https://www.kaggle.com/ayushggarg/covid19-vaccine-adverse-reactions?select=2021VAERSSYMPTOMS.csv)
• Data from the Vaccine Adverse Event Reporting System (VAERS) created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC)
• Contains 3 data sets:
  1. PATIENTS.CSV
  2. VACCINES.CSV
  3. SYMPTOMS.CSV
• Data sets connected by individual IDs (VAERS_ID)
• VAERS User Guide (https://vaers.hhs.gov/docs/VAERSDataUseGuide_November2020.pdf)
  • A guide to variable names

PATIENTS.CSV: Contains information about the individuals that received the vaccines

## # A tibble: 3 x 35
##   VAERS_ID RECVDATE  STATE AGE_YRS CAGE_YR CAGE_MO SEX   RPT_DATE   SYMPTOM_TEXT
##   <chr>    <chr>     <chr>   <dbl>   <dbl>   <dbl> <chr> <date>     <chr>       
## 1 0916600  01/01/20… TX         33      33      NA F     NA         "Right side…
## 2 0916601  01/01/20… CA         73      73      NA F     NA         "Approximat…
## 3 0916602  01/01/20… WA         23      23      NA F     NA         "About 15 m…
## # … with 26 more variables: DIED <chr>, DATEDIED <chr>, L_THREAT <chr>,
## #   ER_VISIT <chr>, HOSPITAL <chr>, HOSPDAYS <dbl>, X_STAY <chr>,
## #   DISABLE <chr>, RECOVD <chr>, VAX_DATE <chr>, ONSET_DATE <chr>,
## #   NUMDAYS <dbl>, LAB_DATA <chr>, V_ADMINBY <chr>, V_FUNDBY <chr>,
## #   OTHER_MEDS <chr>, CUR_ILL <chr>, HISTORY <chr>, PRIOR_VAX <chr>,
## #   SPLTTYPE <chr>, FORM_VERS <dbl>, TODAYS_DATE <chr>, BIRTH_DEFECT <chr>,
## #   OFC_VISIT <chr>, ER_ED_VISIT <chr>, ALLERGIES <chr>

Dimensions:

dim(patients)

## [1] 34121    35

VACCINES.CSV: Contains information about the received vaccine

## # A tibble: 3 x 8
##   VAERS_ID VAX_TYPE VAX_MANU VAX_LOT VAX_DOSE_SERIES VAX_ROUTE VAX_SITE VAX_NAME
##   <chr>    <chr>    <chr>    <chr>   <chr>           <chr>     <chr>    <chr>   
## 1 0916600  COVID19  "MODERN… 037K20A 1               IM        LA       COVID19…
## 2 0916601  COVID19  "MODERN… 025L20A 1               IM        RA       COVID19…
## 3 0916602  COVID19  "PFIZER… EL1284  1               IM        LA       COVID19…

Dimensions:

dim(vaccines)

## [1] 34630     8

SYMPTOMS.CSV: Contains information about the symptoms experienced after vaccination

## # A tibble: 3 x 11
##   VAERS_ID SYMPTOM1      SYMPTOMVERSION1 SYMPTOM2   SYMPTOMVERSION2 SYMPTOM3    
##   <chr>    <chr>                   <dbl> <chr>                <dbl> <chr>       
## 1 0916600  Dysphagia                23.1 Epiglotti…            23.1 <NA>        
## 2 0916601  Anxiety                  23.1 Dyspnoea              23.1 <NA>        
## 3 0916602  Chest discom…            23.1 Dysphagia             23.1 Pain in ext…
## # … with 5 more variables: SYMPTOMVERSION3 <dbl>, SYMPTOM4 <chr>,
## #   SYMPTOMVERSION4 <dbl>, SYMPTOM5 <chr>, SYMPTOMVERSION5 <dbl>

Dimensions:

dim(symptoms)

## [1] 48110    11

1. Load data sets (patients, vaccines, symptoms)
2. Clean each data set individually
3. Augment and merge the data sets
4. Make visualizations
5. Do modeling

Patients, vaccines and symptoms datasets:

• Multiple large files → keep them compressed as gz-files and only decompress when reading into R
• Wrong column types automatically assigned by R → manually assign appropriate column types
  
• NA strings (“NA”, “N/A”, “Unknown”, " "…) → assign NAs when loading data

Patients dataset:

• Unwanted dirty/uniformative columns → select(-c(CAGE_YR, CAGE_MO, RPT_DATE … ))
• NAs that should be interpreted as “no” → replace_na(ALLERGIES = “N”)
• Row duplications → distinct()

Vaccines dataset:

• Contains non-COVID19 vaccines → filter(VAX_TYPE == “COVID19”)
• Contains vaccines of unknown manufacturer → filter(VAX_MANU != “UNKNOWN MANUFACTURER”)
• Row duplications → distinct()
• Duplicated IDs → add_count(VAERS_ID) %>% filter(n == 1) %>% select(-n)
• Inconsistent naming of vaccines → rename()
• Redundant and dirty columns → select(-c(VAX_NAME, VAX_LOT))

Symptoms dataset:

• SYMPTOMVERSION1-5 columns are unneccessary → select(-c())

Patients data set:

• Columns containing long string descriptions → Make tidy categorical (Y/N) variables

## # A tibble: 3 x 3
##   VAERS_ID OTHER_MEDS                     TAKES_ANTIINFLAMATORY
##   <chr>    <chr>                          <chr>                
## 1 0916983  <NA>                           N                    
## 2 0916988  Ibuprofen  PM the night before Y                    
## 3 0916996  Clobetasol, Benadryl           N

• Dirty, redundant and uninformative columns → select(-c(ALLERGIES, OTHER_MEDS … ))

Symptoms data set:

• Too many symptoms and dirty → extract top 20 occurring symptoms and turn them into tidy categorical (TRUE/FALSE) columns
• Calculate total number of symptoms per patient → mutate() to add column (N_SYMPTOMS)

• For visualizing, we need the wide format → inner_join(by = VAERS_ID)
• For modelling, symptoms must be in long-format → pivot_longer() to create:
  • SYMPTOM column: top 20 symptom names
  • SYMPTOM_VALUE column: TRUE/FALSE

Exploratory data analysis

• Visualizations with ggplot()
• Reduction of dimensionality (Principal Component Analysis) with prcomp()

Modelling and statistics

• Logistic regression models with glm()
• Proportions tests with chisq.test()

## # A tibble: 3 x 2
##   SEX       n
##   <chr> <int>
## 1 F     24070
## 2 M      8514
## 3 <NA>    828

## # A tibble: 3 x 2
##   VAX_MANU            n
##   <chr>           <int>
## 1 JANSSEN          1106
## 2 MODERNA         16253
## 3 PFIZER-BIONTECH 16053

Hypothesis: two peaks corresponding to the innate and acquired immune response

## # A tibble: 7 x 6
##   term           estimate std.error statistic  p.value odds_ratio
##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)    -9.39      0.161    -58.2    0         0.0000832
## 2 SEXM            0.928     0.0573    16.2    5.01e-59  2.53     
## 3 AGE_YRS         0.0914    0.00207   44.1    0         1.10     
## 4 HAS_ALLERGIESY -0.0209    0.0605    -0.346  7.29e- 1  0.979    
## 5 HAS_ILLNESSY    1.08      0.0653    16.5    6.36e-61  2.93     
## 6 HAS_COVIDY     -0.113     0.142     -0.795  4.27e- 1  0.893    
## 7 HAD_COVIDY     -0.00340   0.195     -0.0175 9.86e- 1  0.997

## # A tibble: 20 x 6
##   term          estimate std.error statistic  p.value odds_ratio
##   <chr>            <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)     -2.01     0.0287    -70.1  0             0.134
## 2 HEADACHETRUE    -1.67     0.156     -10.7  7.92e-27      0.188
## 3 PYREXIATRUE     -0.429    0.112      -3.82 1.34e- 4      0.651
## 4 CHILLSTRUE      -1.21     0.171      -7.11 1.17e-12      0.298
## 5 FATIGUETRUE     -0.367    0.115      -3.19 1.41e- 3      0.693
## 6 PAINTRUE        -0.913    0.153      -5.98 2.17e- 9      0.401
## 7 NAUSEATRUE      -0.621    0.139      -4.46 8.17e- 6      0.538
## 8 DIZZINESSTRUE   -2.17     0.193     -11.2  2.87e-29      0.114
## # … with 12 more rows

## # A tibble: 20 x 9
##   SYMPTOM  estimate std.error statistic p.value conf.low conf.high odds_ratio
##   <chr>       <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>      <dbl>
## 1 HEADACHE  -0.164     0.0987    -1.67   0.0958   -0.362    0.0255      0.848
## 2 PYREXIA    0.0152    0.102      0.150  0.881    -0.189    0.211       1.02 
## 3 CHILLS    -0.121     0.109     -1.11   0.266    -0.340    0.0875      0.886
## 4 FATIGUE    0.0565    0.105      0.539  0.590    -0.154    0.258       1.06 
## 5 PAIN       0.0113    0.110      0.102  0.919    -0.210    0.222       1.01 
## # … with 15 more rows, and 1 more variable: identified_as <chr>

Chi-squared contingency table tests

Important verbs and tools used:

• prcomp()
• kmeans()
• tidymodels: (used for what?)